Multi-time-scale input approaches for hourly-scale rainfall-runoff modeling based on recurrent neural networks

2021-11-01
Ishida, Kei
Kiyama, Masato
Ercan, Ali
Amagasaki, Motoki
Tu, Tongbi
This study proposes two effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as input. One approach provides coarse and fine temporal resolutions of the input time-series data to RNN in parallel. The other concatenates the coarse and fine temporal resolutions of the input time-series data over time before considering them as the input to RNN. In both approaches, first, the finer temporal resolution data are utilized to learn the fine temporal scale behavior of the target data. Then, coarser temporal resolution data are expected to capture long-duration dependencies between the input and target variables. The proposed approaches were implemented for hourly rainfall-runoff modeling at a snow-dominated watershed by employing a long short-term memory network, which is a type of RNN. Subsequently, the daily and hourly meteorological data were utilized as the input, and hourly flow discharge was considered as the target data. The results confirm that both of the proposed approaches can reduce the required computational time for the training of RNN significantly. Lastly, one of the proposed approaches improves the estimation accuracy considerably in addition to computational efficiency.
JOURNAL OF HYDROINFORMATICS

Suggestions

Capabilities of deep learning models on learning physical relationships: Case of rainfall-runoff modeling with LSTM
Yokoo, Kazuki; Ishida, Kei; Ercan, Ali; Tu, Tongbi; Nagasato, Takeyoshi; Kiyama, Masato; Amagasaki, Motoki (2022-01-01)
ABSTR A C T This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff modeling in a snow-dominated watershed by means of a long short-term memory (LSTM) network is selected. Daily precipitation and mean air temperature were used as model input to estimate daily flow discharge. After model training and verification, two experimental simulations were con-ducted with hypothetical inputs instead of observed meteorologic...
Deep Learning-Enabled Technologies for Bioimage Analysis
Rabbi, Fazle; Dabbagh, Sajjad Rahmani; Angın, Pelin; Yetisen, Ali Kemal; Tasoglu, Savas (2022-02-01)
Deep learning (DL) is a subfield of machine learning (ML), which has recently demon-strated its potency to significantly improve the quantification and classification workflows in bio-medical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of em...
Sampling Performance of Multiple Independent Molecular Dynamics Simulations of an RNA Aptamer
Yan, Shuting; Peck, Jason M.; İlgü, Müslüm; Nilsen-Hamilton, Marit; Lamm, Monica H. (2020-08-01)
Using multiple independent simulations instead of one long simulation has been shown to improve the sampling performance attained with the molecular dynamics (MD) simulation method. However, it is generally not known how long each independent simulation should be, how many independent simulations should be used, or to what extent either of these factors affects the overall sampling performance achieved for a given system. The goal of the present study was to assess the sampling performance of multiple indep...
Multiobjective evolutionary feature subset selection algorithm for binary classification
Deniz Kızılöz, Firdevsi Ayça; Coşar, Ahmet; Dökeroğlu, Tansel; Department of Computer Engineering (2016)
This thesis investigates the performance of multiobjective feature subset selection (FSS) algorithms combined with the state-of-the-art machine learning techniques for binary classification problem. Recent studies try to improve the accuracy of classification by including all of the features in the dataset, neglecting to determine the best performing subset of features. However, for some problems, the number of features may reach thousands, which will cause too much computation power to be consumed during t...
Deep Learning-Based Hybrid Approach for Phase Retrieval
IŞIL, ÇAĞATAY; Öktem, Sevinç Figen; KOÇ, AYKUT (2019-06-24)
We develop a phase retrieval algorithm that utilizes the hybrid-input-output (HIO) algorithm with a deep neural network (DNN). The DNN architecture, which is trained to remove the artifacts of HIO, is used iteratively with HIO to improve the reconstructions. The results demonstrate the effectiveness of the approach with little additional cost.
Citation Formats
K. Ishida, M. Kiyama, A. Ercan, M. Amagasaki, and T. Tu, “Multi-time-scale input approaches for hourly-scale rainfall-runoff modeling based on recurrent neural networks,” JOURNAL OF HYDROINFORMATICS, vol. 23, no. 6, pp. 1312–1324, 2021, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/100983.